识别和评估基于 MOR 的 CMIP6 模型的新方法,用于无测站流域的水文分析

IF 3.2 3区 地球科学 Q1 Environmental Science
Dibyandu Roy, Anirban Dhar, Venkappayya R. Desai
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引用次数: 0

摘要

考虑到季风季节每年的时空变化,确定季风季节的来临和消退日期是一个至关重要的复杂现象。季风季节对降雨量、水源补充和水文系统贡献巨大,但也会造成极端水文现象,尤其是对东南亚的高海拔流域而言。基于全球环流模型(GCM)--耦合模型相互比较项目第 6 阶段(CMIP6)的降雨量和温度数据有助于充分反映现在和未来的气候情景。然而,需要对未经校正的 GCM-CMIP6 数据集的可用性进行区域评估。本研究的重点是根据季风开始(MO)和消退(MR)日期以及其他气候学时间参数确定最合适的 GCM-CMIP6。为找到最合适的 GCM-CMIP6(即 CMCC-ESM2),制定了 MO 和 MR 的数值定义。在此背景下,使用基于季风起始-恢复 (MOR) 的最合适 GCM 的气象输入进行了径流模拟,以评估其可用性。在观测数据集的基础上,采用多模型模拟方法进行径流估算,以找到更合适的水文模型。所提出的整体方法在一个水文极端易发的无测站流域(即 Ranikhola)进行了测试。根据统计演化(R2 [0.693]、NSE [0.662] 和 RSR [0.581]),CMCC-ESM2 和 SSP2-4.5 被确定为最适合未来日径流预测的 SSP。未来水文分析表明,未来几年(2021-2049 年)季风平均峰值径流量将比校核期(2015-2020 年)增加 18.01%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach for Identifying and Assessing MOR-Based CMIP6 Model for Hydrological Analysis in an Ungauged Watershed

The identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high-altitude watersheds in Southeast Asia. Global Circulation Model (GCM)-Coupled Model Intercomparison Project Phase 6 (CMIP6)-based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM-CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best-suited GCM-CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best-suited GCM-CMIP6 (i.e., CMCC-ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset-retreat (MOR)-based best-suited GCM to evaluate its usability. A multi-model simulation approach has been carried out for runoff estimation based on observed datasets to find a better-suited hydrological model. The proposed overall methodology is tested in a hydrological extreme-prone ungauged watershed (i.e., Ranikhola). CMCC-ESM2 and SSP2-4.5 has been identified as best-suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049).

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来源期刊
Hydrological Processes
Hydrological Processes 环境科学-水资源
CiteScore
6.00
自引率
12.50%
发文量
313
审稿时长
2-4 weeks
期刊介绍: Hydrological Processes is an international journal that publishes original scientific papers advancing understanding of the mechanisms underlying the movement and storage of water in the environment, and the interaction of water with geological, biogeochemical, atmospheric and ecological systems. Not all papers related to water resources are appropriate for submission to this journal; rather we seek papers that clearly articulate the role(s) of hydrological processes.
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